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Markiewicz-Gospodarek A, Markiewicz R, Borowski B, Dobrowolska B, Łoza B. Self-Regulatory Neuronal Mechanisms and Long-Term Challenges in Schizophrenia Treatment. Brain Sci 2023; 13:brainsci13040651. [PMID: 37190616 DOI: 10.3390/brainsci13040651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/04/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
Schizophrenia is a chronic and relapsing disorder that is characterized not only by delusions and hallucinations but also mainly by the progressive development of cognitive and social deficits. These deficits are related to impaired synaptic plasticity and impaired neurotransmission in the nervous system. Currently, technological innovations and medical advances make it possible to use various self-regulatory methods to improve impaired synaptic plasticity. To evaluate the therapeutic effect of various rehabilitation methods, we reviewed methods that modify synaptic plasticity and improve the cognitive and executive processes of patients with a diagnosis of schizophrenia. PubMed, Scopus, and Google Scholar bibliographic databases were searched with the keywords mentioned below. A total of 555 records were identified. Modern methods of schizophrenia therapy with neuroplastic potential, including neurofeedback, transcranial magnetic stimulation, transcranial direct current stimulation, vagus nerve stimulation, virtual reality therapy, and cognitive remediation therapy, were reviewed and analyzed. Since randomized controlled studies of long-term schizophrenia treatment do not exceed 2-3 years, and the pharmacological treatment itself has an incompletely estimated benefit-risk ratio, treatment methods based on other paradigms, including neuronal self-regulatory and neural plasticity mechanisms, should be considered. Methods available for monitoring neuroplastic effects in vivo (e.g., fMRI, neuropeptides in serum), as well as unfavorable parameters (e.g., features of the metabolic syndrome), enable individualized monitoring of the effectiveness of long-term treatment of schizophrenia.
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Affiliation(s)
| | - Renata Markiewicz
- Department of Neurology, Neurological and Psychiatric Nursing, Medical University of Lublin, 20-093 Lublin, Poland
| | - Bartosz Borowski
- Students Scientific Association at the Department of Human Anatomy, Medical University of Lublin, 20-090 Lublin, Poland
| | - Beata Dobrowolska
- Department of Holistic Care and Management in Nursing, Medical University of Lublin, 20-081 Lublin, Poland
| | - Bartosz Łoza
- Department of Psychiatry, Medical University of Warsaw, 02-091 Warsaw, Poland
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Bai D, Yao W, Wang S, Yan W, Wang J. Recurrence network analysis of schizophrenia MEG under different stimulation states. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Dabiri M, Dehghani Firouzabadi F, Yang K, Barker PB, Lee RR, Yousem DM. Neuroimaging in schizophrenia: A review article. Front Neurosci 2022; 16:1042814. [PMID: 36458043 PMCID: PMC9706110 DOI: 10.3389/fnins.2022.1042814] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/28/2022] [Indexed: 11/16/2022] Open
Abstract
In this review article we have consolidated the imaging literature of patients with schizophrenia across the full spectrum of modalities in radiology including computed tomography (CT), morphologic magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and magnetoencephalography (MEG). We look at the impact of various subtypes of schizophrenia on imaging findings and the changes that occur with medical and transcranial magnetic stimulation (TMS) therapy. Our goal was a comprehensive multimodality summary of the findings of state-of-the-art imaging in untreated and treated patients with schizophrenia. Clinical imaging in schizophrenia is used to exclude structural lesions which may produce symptoms that may mimic those of patients with schizophrenia. Nonetheless one finds global volume loss in the brains of patients with schizophrenia with associated increased cerebrospinal fluid (CSF) volume and decreased gray matter volume. These features may be influenced by the duration of disease and or medication use. For functional studies, be they fluorodeoxyglucose positron emission tomography (FDG PET), rs-fMRI, task-based fMRI, diffusion tensor imaging (DTI) or MEG there generally is hypoactivation and disconnection between brain regions. However, these findings may vary depending upon the negative or positive symptomatology manifested in the patients. MR spectroscopy generally shows low N-acetylaspartate from neuronal loss and low glutamine (a neuroexcitatory marker) but glutathione may be elevated, particularly in non-treatment responders. The literature in schizophrenia is difficult to evaluate because age, gender, symptomatology, comorbidities, therapy use, disease duration, substance abuse, and coexisting other psychiatric disorders have not been adequately controlled for, even in large studies and meta-analyses.
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Affiliation(s)
- Mona Dabiri
- Department of Radiology, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Kun Yang
- Department of Psychiatry, Molecular Psychiatry Program, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Peter B. Barker
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, MD, United States
| | - Roland R. Lee
- Department of Radiology, UCSD/VA Medical Center, San Diego, CA, United States
| | - David M. Yousem
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, MD, United States
- *Correspondence: David M. Yousem,
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Fred AL, Kumar SN, Kumar Haridhas A, Ghosh S, Purushothaman Bhuvana H, Sim WKJ, Vimalan V, Givo FAS, Jousmäki V, Padmanabhan P, Gulyás B. A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications. Brain Sci 2022; 12:brainsci12060788. [PMID: 35741673 PMCID: PMC9221302 DOI: 10.3390/brainsci12060788] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/01/2022] [Accepted: 06/08/2022] [Indexed: 11/30/2022] Open
Abstract
Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNRMEG = 2.2 db, SNREEG < 1 db) and spatial resolution (SRMEG = 2−3 mm, SREEG = 7−10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single-channel connectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics.
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Affiliation(s)
- Alfred Lenin Fred
- Department of CSE, Mar Ephraem College of Engineering and Technology, Marthandam 629171, Tamil Nadu, India; (A.L.F.); (F.A.S.G.)
| | | | - Ajay Kumar Haridhas
- Department of ECE, Mar Ephraem College of Engineering and Technology, Marthandam 629171, Tamil Nadu, India;
| | - Sayantan Ghosh
- Department of Integrative Biology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India;
| | - Harishita Purushothaman Bhuvana
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
| | - Wei Khang Jeremy Sim
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
| | - Vijayaragavan Vimalan
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
| | - Fredin Arun Sedly Givo
- Department of CSE, Mar Ephraem College of Engineering and Technology, Marthandam 629171, Tamil Nadu, India; (A.L.F.); (F.A.S.G.)
| | - Veikko Jousmäki
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Aalto NeuroImaging, Department of Neuroscience and Biomedical Engineering, Aalto University, 12200 Espoo, Finland
| | - Parasuraman Padmanabhan
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
- Correspondence: (P.P.); (B.G.)
| | - Balázs Gulyás
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
- Department of Clinical Neuroscience, Karolinska Institute, 17176 Stockholm, Sweden
- Correspondence: (P.P.); (B.G.)
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Bai D, Yao W, Wang S, Wang J. Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms. ENTROPY 2022; 24:e24030314. [PMID: 35327825 PMCID: PMC8946927 DOI: 10.3390/e24030314] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 02/19/2022] [Accepted: 02/20/2022] [Indexed: 12/27/2022]
Abstract
Schizophrenia is a neuropsychiatric disease that affects the nonlinear dynamics of brain activity. The primary objective of this study was to explore the complexity of magnetoencephalograms (MEG) in patients with schizophrenia. We combined a multiscale method and weighted permutation entropy to characterize MEG signals from 19 schizophrenia patients and 16 healthy controls. When the scale was larger than 42, the MEG signals of schizophrenia patients were significantly more complex than those of healthy controls (p<0.004). The difference in complexity between patients with schizophrenia and the controls was strongest in the frontal and occipital areas (p<0.001), and there was almost no difference in the central area. In addition, the results showed that the dynamic range of MEG complexity is wider in healthy individuals than in people with schizophrenia. Overall, the multiscale weighted permutation entropy method reliably quantified the complexity of MEG from schizophrenia patients, contributing to the development of potential magnetoencephalographic biomarkers for schizophrenia.
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Affiliation(s)
- Dengxuan Bai
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
| | - Wenpo Yao
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- Correspondence: (W.Y.); (J.W.)
| | - Shuwang Wang
- School of Electronic Information, Nanjing Vocational College of Information Technolog, Nanjing 210023, China;
| | - Jun Wang
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- Correspondence: (W.Y.); (J.W.)
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